What caused the sudden 30% increase in appointment cancellations for Providence's Express Care clinics last week?

Introduction

The sudden 30% increase in appointment cancellations for Providence's Express Care clinics last week is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for the service.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product ecosystem and user journey. From there, I'll break down the metric, gather relevant data, form hypotheses, and conduct a thorough root cause analysis. Finally, I'll propose validation methods and outline a comprehensive resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be a recent change. Has there been any significant update to the Express Care booking system in the past month?

Why it matters: Recent changes often correlate with sudden metric shifts. Expected answer: Yes, a new version was deployed two weeks ago. Impact on approach: If true, I'd focus on technical issues and user experience changes.

• Considering user segments, I'm curious about the distribution. Are we seeing this increase across all patient demographics, or is it concentrated in specific groups?

Why it matters: Helps identify if the issue is universal or segment-specific. Expected answer: The increase is more pronounced among older patients. Impact on approach: If true, I'd investigate accessibility issues or communication gaps.

• Thinking about external factors, has there been any change in local health policies or insurance coverage that might affect Express Care usage?

Why it matters: External policy changes can significantly impact healthcare service utilization. Expected answer: No major policy changes have been reported. Impact on approach: If true, I'd focus more on internal factors and user behavior.

• Regarding the metric itself, I'm wondering about the definition. Has there been any change in how we define or measure a "cancellation" in our system?

Why it matters: Ensures we're comparing apples to apples in our data analysis. Expected answer: The definition has remained consistent. Impact on approach: If changed, I'd need to recalibrate our analysis based on the new definition.